In the first ten years after establishment of the Japan Aerospace eXploration Agency (JAXA) in 2003, our focuses were mainly on technical development (hardware and software) and accumulation of application research. In the next decade, we focus more on solution on social issues using innovative space science technology. Currently, JAXA is operating and developing several earth observation satellites and sensors: Greenhouse gases Observing SATellite (GOSAT) "IBUKI",
Global Change Observation Mission
- Water "SHIZUKU" (GCOM-W),
Global Precipitation Measurement/Dual-
frequency Precipitation Radar (GPM/DPR), Advanced Land Observing Satellite-2 "DAICHI-2" (ALOS-2), Global Change Observation Mission - Climate (GCOM-C), Earth Cloud, Aerosol and Radiation Explorer (EarthCARE), and GOSAT-2. They will provide essential environmental parameters, such as aerosols, clouds, land vegetation, ocean color, GHGs, and so on. In addition to the above missions, we are studying new instruments (altimeter, LIDAR, detectors, optical components) to obtain new parameters. Our activities will advance to provide essential inputs for diagnosis, prediction, and management of climate change, environmental assessment, and disaster monitoring.

Climatological characteristics of summer daytime precipitation and its signals of visible infrared in ice, mixed and water
phase in tropics and subtropics are investigated for through merged VIRS and PR data from 1998 to 2012. Results
indicate that the frequency of precipitation in ice phase varies from 1% to 20%, which is at least 2% higher than that in
mixed phase over the tropical land and ocean. For precipitation in water phase, the frequency is about 1% uniformly
distributed over ocean. In the region of Asian Summer Monsoon, especially in the Bay of Bengal and South China Sea,
prosperous precipitation in ice phase is obvious against the higher in mixed phase over East China. The precipitation in
the region of ITCZ, especially in the western and eastern Pacific Ocean, is mainly in ice phase, which suggests the
stronger convective activities over this region. In the mid-latitude of southern hemisphere, much more mixed-phase
precipitation occurs, which should be related to activities of cold front systems. At VIRS channel 0.63 μm and its ratio to
channel 1.6 μm, significant differences appear among the three phases, which suggests a simple threshold method to
classify precipitation in the three phases by using the ratio. Over ocean, the mean height of rain top detected by PR
shows 7.0 km, 5.5 km and 3.0 km for precipitation in phases of ice, mixed and water, respectively, which is at least 0.5
km lower than those over land. Over the Tibet Plateau, the mean rain top can reach over 7.5 km for precipitation in the
three phases. Studies indicate that large mean rain rate, over 4.5 mm/h, is represented by precipitation in ice phase
comparing with that in the other two phases. The vertical structures in the contoured frequency by altitude diagram
(CFAD) also reveal big differences among precipitation in the three phases over land and ocean. However, statistics for
relationship between near surface rain rate and signals at each VIRS channels show that the ratio of reflectivity at 0.63
μm to 1.65 μm may be a good approach to retrieve rain rate for precipitation in different phases.

Cloud Amount (CA) is the dominant modulator of radiative fluxes. Satellite and ground observations act as two main
sources for the global cloud climatology. In this study, we analyze the comparability of these two datasets over China.
The MODIS cloud mask products which provide the pixel-based flag of cloudy or not is used to calculate satellite
derived CA, while ground based CA is obtained from Synop stations which are visually estimated by observers. To
match surface observations with MODIS data for comparison, a prerequisite is to determine the Effective Field of View
(EFOV) of the ground observer. Instead of setting a constant EFOV, we firstly vary the radius of FOV for correlation
analysis and find that a radius of 60 km is most useful for comparison purpose. The correlation coefficient ranges from
0.53 to 0.81 for different seasons, suggesting a significant relationship between satellite and ground based CA. Secondly,
based on the estimated EFOV over China, an index of Effective Cloud Observation Density (ECOD) is introduced to
evaluate the spatial distribution of Synop stations and the results show that western China has much lower ECOD than
the eastern part. Finally, cloud fraction map and frequency distribution are applied to compare the two CA datasets and
both indicate that MODIS derived and surface observed CA have similar spatial distribution, while obvious difference
occurs at both high and low values.

Satellite-based products increasingly take an important role in filling data gaps in data sparse regions around the
world. In recent years, precipitation products that utilize multi-satellite and multi-sensor datasets have been gaining
more popularity than products from a single sensor or satellite. Adjusted with gauge and ground radar data, satellitebased
products have been significantly improved. However the history of satellite-based precipitation products is
relatively short compared to the length of 30 years in the definition for climatology from the World Meteorological
Organization (WMO). For example, the NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) has been in
operation for over 16 years since 1997. The length of TRMM is far shorter than those from ground observations,
raising a question whether TRMM climatology products are good enough for research and applications. In this
study, three climatologies derived from ground observations (Global Precipitation Climatology Centre (GPCC) and
Willmott and Matsuura (WM)) and a blended product (the TRMM Multi-Satellite Precipitation Analysis (TMPA)
monthly product or 3B43) are compared on a global scale to assess the performance and weaknesses of the TMPAderived
climatology. Results show that the 3B43 climatology matches well with the two gauge-based climatologies
in all seasons in terms of spatial distribution, zonal means as well as seasonal variations. However, high variations in
rain rates are found in light rain regions such as the Sahara Desert. Large negative biases (3B43<WM<GPCC) are
found in some high rain rate regions, which is not well understood. Further investigations are needed.

Over complex terrains, ground radars usually rely on scans at higher elevation angles to observe precipitating systems.
The surface quantitative precipitation estimation (QPE) might have considerable errors if veridical structure of
precipitation is not considered because radar reflectivity varies with height due to evaporation at low levels as well as
processes of melting, aggregation, and drop break-up. The vertical profile of reflectivity (VPR) links the surface
precipitation to the radar observation at higher levels, which is very useful for accurately estimating the surface rainfall.
Researchers at the University of Oklahoma have demonstrated the integration of the Tropical Rainfall Measurement
Mission (TRMM) Precipitation Radar products (4-km precipitation quantity, types, and 250-meter vertical profile of
reflectivity (VPR)) into the NEXRAD ground-based radar rainfall estimation system. In the latest progress in the VPRIdentification
and Enhancement (VPR-IE) approach, we have optimally combined the climatological VPR information to
the National Mosaic QPE (NMQ) system from 1 January 2011 to 31 December 2011 over the Mountainous West Region
of the U.S. Performance of latest VPR-IE is systematically evaluated by rain gauges measurements for different
precipitation types. The results indicate improvements in precipitation detection and estimation following the incorporation
of space-based radar information into ground radar networks.

Both satellite-based 11-um infrared window channel brightness temperature (IRW BT) and 6-7-um water vapor minus
IRW BT difference (WV-IRW BTD) have been widely used in detection of convective overshooting top (OT). In this
paper, considering a significant negative correlation between WV-IRW BTD and IRW BT (<0.7 in 93.07% cases) but a
banded distribution, a combined parameter named weighted BTD (WBTD) is constructed for convective OT detection,
which offers an improvement over the WV-IRW BTD technique. With this method, MTSAT-1R satellite data and ground
station precipitation data during July to September in 2010 in east China are used to research the relationship between
OT occurrence and precipitation, also to study the variations of precipitation intensity (PI) and mean WBTD (WBTD_M)
during the evolution of convective clouds. The results show that (1) 58.2% of the analyzed OT cases appear a certain
rainfall and 46.32% of these precipitation events record over 1mm/10min PI; (2) in all recorded over 1mm/10min
precipitation events, OT occurrence probability in the corresponding region is 12.55%, greater probability happens in
higher PI, in over 10mm/10min cases, the probability rises to 34%; (3) study in ten groups of strong convective clouds
shows a positive correlation (up to 0.9) between WBTD_OI and PI; OT occurrence probability in cloud area is great
when convection develops to a strong phase, while very small even no OTs in the early and late of convection.

A suite of products has been developed and evaluated to assess hazards presented by convective storm downbursts
derived from the current generation of Geostationary Operational Environmental Satellite (GOES) (13-15). The existing
suite of GOES downburst prediction products employs the GOES sounder to calculate risk based on conceptual models
of favorable environmental profiles for convective downburst generation. A diagnostic nowcasting product, the
Microburst Windspeed Potential Index (MWPI), is designed to infer attributes of a favorable downburst environment: 1)
the presence of large convective available potential energy (CAPE), and 2) the presence of a surface-based or elevated
mixed layer with a steep temperature lapse rate and vertical relative humidity gradient. These conditions foster intense
convective downdrafts upon the interaction of sub-saturated air in the elevated or sub-cloud mixed layer with the storm
precipitation core. This paper provides an updated assessment of the MWPI algorithm, presents recent case studies
demonstrating effective operational use of the MWPI product over the Atlantic coastal region, and presents validation
results for the United States Great Plains and Mid-Atlantic coastal region. In addition, an application of the brightness
temperature difference (BTD) between GOES imager water vapor (6.5μm) and thermal infrared (11μm) channels that
identifies regions where downbursts are likely to develop, due to mid-tropospheric dry air entrainment, will be outlined.

The Vertical Atmospheric Sounding Suits (VASS) onboard FY-3C satellite includes The Infrared Atmospheric
Sounder (IRAS), Microwave Temperature Sounder (MWTS) and Microwave Temperature and moisture sounder
(MWTHS-II). The IRAS is similar to that onboard FY-3A/B, while the MWTS-II/MWTHS-II is more sophisticated
than their precursors. MWTS has 13 channels mainly at the window region and 57 O2 absorption band, and
MWTHS has 15 channels mainly at the 118 O2 absorption band and the 183 H2O absorption band. A package has
been developed to retrieve the atmospheric temperature profile, moisture profile, atmospheric total ozone, and
other parameters in both clear and cloudy atmospheres from the VASS measurements, which is remap to IRAS
Field of view. The algorithm that retrieves these parameters contains four steps: 1) cloud and precipitation
detection, 2) bias adjustment for VASS measurements, 3) regression retrieval processes, and 4) a nonlinear iterative
physical retrieval. The package does not process precipitation FOV, and for non-precipitation cloud FOV, the
measurements from middle to low channels of IRAS are excluded. Till now all instruments are under orbit
examination stage, and the primary results show that temperature soundings can be produced under partial cloud
cover with RMS errors on the order of, or better than, 2.0 K in 1-km-thick layers from the surface to 700 mb, 1-km
layers from 700–300 mb, 3-km layers from 300–30 mb, and 5-km layers from 30–1 mb; and moisture profiles can
be obtained with an accuracy better than 20% absolute errors in 2-km layers from the surface to nearly 300 mb.

In this work, the super-thin cloud detection algorithm [1], that uses the polarization angle of the backscattered solar
radiation to find the super-thin clouds, is briefly reviewed and the retrieval of the optical thickness of these clouds is
proposed. We found that at the neighborhood angles of the backscattering direction, these clouds can be reliably
detected. The polarized components of the reflected light may be used to retrieve the optical thickness of these
clouds.

Retrieval errors of the atmospheric composition using optical methods (DOAS et al.) are under the determining influence
of the cloudiness during the measurements. If there is information about the clouds, the optical model of the atmosphere
used to interpret the measurements can be adjusted, and the retrieval errors are reduced.
For the reconstruction of the parameters of clouds a method was developed based on taking pictures of the sky by a pair
of digital photocameras and subsequent processing of the obtained sequence of stereo frames by a method of
morphological analysis of images.
Since the directions of the optical axes of the cameras are not exactly known, the calibration of the direction of sight of
the cameras was conducted at the first stage using the photographs of the stars in the night sky. At the second stage, the
relative shift of the image of the cloud fragment on the second frame of the pair was calculated. Stereo pairs obtained by
simultaneous photography, allowed us to estimate the height of cloud.
The report describes a mathematical model of measurement, pose and solve the problem of calibration of direction of
sight of the cameras, describes methods of combining of image fragments by morphological method, the problem of
estimating cloud height and speed of their movement is formulated and solved. The examples of evaluations in a real
photo are analyzed and validated by the way of comparing with the results of measurement by laser rangefinder.

Most of the Antarctic continent is covered with ice and snow. However, it’s hard to distinguish clouds from ice and snow
in remote sensing images because they both have similar characteristics in visible reflectances and infrared brightness
temperatures. Thus there exist great difficulties in determining the precise locations and distribution of clouds in remote
sensing images. To solve this problem, a new method is proposed to identify clouds for Landsat imagery over the
Antarctic region. Top of atmosphere reflectance and brightness temperature of Landsat imagery are used as inputs.
Several spectral tests combining with morphological operations are employed to highlight clouds, especially thin clouds.
The results show that the new method can not only greatly eliminate the effects of snow and ice, but also extract thin
clouds effectively, and thus improve cloud detection accuracy over the Antarctic region.

From 2004 to 2013, PARASOL had offered multi-angle (up to 16) polarized and intensity measurements in nine bands.
In the paper, based on MISR’s multi-angle algorithm, aerosol is retrieved from multi-angle intensity measurements of
PARASOL. For the application of MISR’s multi-angle algorithm, the atmospheric correction of PARASOL intensity
data was done in Beijing area. It is shown that in green, red and near infrared bands, the normalized reflectance are close,
but that in blue band are not stable. So, in our algorithm, the measurements in green, red and near infrared bands are
selected to retrieve aerosol. We retrieved aerosol as follow: 1) For the aerosol mixed with fine and coarse particles, we
revise the surface reflectance from PARASOL multi-angle intensity data in the grid of aerosol optical depth (AOD) from
0 to 1.8; 2) with the variance between normalized reflectance and its band average, an overall residual for each AOD is
worked out; 3) the minimum residual provides the values for AOD. Then, we retrieved aerosol over Beijing area in 2009.
The correlation coefficient between our retrieved and AERONET Beijing measured AOD is more than 0.7, and AODs
from our algorithm is obviously smaller than that from ground-based measurements.

A multi-wavelength Raman lidar system which includes both vibrational rotational Raman and Mie scattering spectra
has been designed and described. A retrieval algorithm for water vapor and temperature has also been developed based
on the potential observations from this Raman lidar system. The performance of this retrieval method and the new lidar
system has been evaluated with a synthetic test. Using the U.S. standard atmosphere model and main parameters of this
lidar system, we have obtained signal to noise ratio（SNR）of water-vapor backscatter signals under different
circumstances of aerosol content, pulse emission energy and signal integration time. With the model calculated
backscatter signals, both atmospheric water-vapor and temperature profiles have been retrieved and their uncertainties
have been analyzed. These synthetic tests indicate that our new lidar system can obtain profiles of water-vapor and
temperature at both day and night time, but with different detection heights. The retrieval algorithm shows less than 30%
relative error for water vapor mixing ratio and good accuracy with a minimum detection of temperature less than 2 K.

The spatial and temporal variations in regional aerosol optical thickness (AOT) over China during 2013 were
investigated in this study using Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol intermediate product (IP)
data obtained from the NOAA CLASS. It is found that high level AOT in China mainly occurs in the spring and
summer. The study compared the aerosols in the economically developed eastern China to those in the western region;
urban areas versus rural areas; inland versus coastal cities. Further investigation was also performed to validate VIIRS
derived AOT data and aerosol type with in situ ground measurements.

Aerosols in the atmosphere, including dust and pollutants, scatters/absorbs solar radiation and change the microphysics
of clouds, thus influencing the Earth’s energy budget, climate, air quality, visibility, agriculture and water circulation.
Pollutants have also been reported to threaten the human health. The present research collaborated with the U.S. NASA
and the U.S. Aerosol Robotic Network (AERONET) is to study the aerosol characteristics in East Asia and improve the
long-distance transportation monitoring technology by analyzing the observations of aerosol characteristics in East Asia
during Distributed Regional Aerosol Gridded Observation Networks (DRAGON) Campaign (March 2012-May 2012).
The sun photometers that measure the aerosol optical characteristics were placed evenly throughout the Korean
Peninsula and concentrated in Seoul and the metropolitan area. Observation data are obtained from the DRAGON
campaign and the first year (2012) observation data (aerosol optical depth and aerosol spatial distribution) are analyzed.
Sun photometer observations, including aerosol optical depth (AOD), are utilized to validate satellite observations from
Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS). Additional
analysis is performed associated with the Northeast Asia, the Korean Peninsula in particular, to determine the spatial
distribution of the aerosol.

Ion mobility spectrometry (IMS) is an attractive material analysis technology for developing a miniaturized volatile
organic compounds (VOCs) on-site monitoring sensor. Having simple instrumentation, IMS is especially suitable when
portability and sensitivity are required. In this work, we designed an ion mobility spectrometer with UV ionization. The
geometric parameters of the UV-IMS were optimized based on a numerical simulation. The simulation results
demonstrated that the drift electric field in the drift region was approximately homogenous and in the reaction region had
an ion focusing effect, which could improve the sensitivity and resolving power of the IMS. The UV-IMS has been
constructed and used to detect VOCs, such as acetone, benzene, toluene and m-xylene (BTX). The resolution of these
substance measured from the UV-IMS in the atmospheric conditions are about 30 and the limit of detection (LOD) is low to
ppmv. The ion mobility module and electric circuit are integrated in a main PCB, which can facilitate mass production and
miniaturization. The present UV-IMS is expected to become a tool of choice for the on-site monitoring for VOCs.

Blowing snow plays an important role in the studies of the Earth’s cryosphere. Not only can
it affects the ice sheet mass balance and hydrological processes through redistributing surface mass
and driving spatial and temporal variations in snow accumulation, it also has a significant impact on
the long wave radiation budget both at the surface and at the top of the atmosphere. In this article,
we show that blowing snow has substantial impact on the Antarctic Outgoing Longwave Radiation
(OLR). Significant cloud-free OLR differences are observed between the clear and blowing snow
sky, with the sign and magnitude depending on season and time of the day.

Cloud Occurrence Frequency (COF) of different cloud types from three cloud classification products are evaluated in
this study over six sub-regions of China in 2012, including surface products, Feng-Yun-2E (FY-2E) and CloudSat satellite
products. The spatial distribution and seasonal variation of cloud types are compared on the macro-characteristics. The
main cloud types are cirrus (Ci), cirrostratus (Cs), stratocumulus (Sc), altocumulus (Ac), altostratus (As), cumulus (Cu)
and nimbostratus (Ns). The results of this study show that the surface observation in general overestimates a lot more than
FY-2E and CloudSat; FY-2E provides more accurate high cloud estimates; CloudSat provides better middle cloud type
detection; surface and CloudSat observation capture more consistent distribution of Sc, Ac, Cu and high cloud; all three
datasets provide similar evidence on seasonal cycle, especially on Cu, Cb, Ac, with the pattern of peak in summer, their
COF seasonal variation decrease. Seasonal variations of Sc, As, Ns COF especially from CloudSat are more reasonable.
Cloud type and frequency is also compared among the three datasets by using limited temporal and spatial thresholds. In
general, all three datasets yield more consistent detection of high cloud types than the other cloud types. Although this
paper emphasizes on the comparison of spatial distribution, seasonal variation and coincident samples, it also discusses the
strengths and weakness of different passive/active techniques.

The objective of this study was to integrate the advantages of multi-source remote sensing to
monitor dust storms and better discriminate between regions where dust storms occur. Firstly, The
traditional evidence theory algorithm was improved by not only considering the certainty of the
evidence, but also considering the average level of support for the subsets of evidence in the
discrimination framework in the process of evidence combination by reducing the conflict between
synthesized data. Then the algorithm is applied to the FY-2E infrared difference dust index (IDDI)
and the FY-3A dust strength index (DSI) to categorize the study region as either a dust storm area,
non-dust storm area, or possible dust storm area. Finally, the result was validated and analyzed using
the monitored data from ground stations. Both the accuracy and reliability of the dust monitoring
results were considerably improved using our method.

The global carbon dioxide observation satellite (TanSat) mission of China is introduced. Two instruments carried by
TanSat including carbon dioxide (CO2) spectrometer with high spectral resolution, termed the TanSat CO2 Spectrometer
(TSCS), and the Cloud and Aerosol Polarize Instrument (CAPI) will make global measurements of atmospheric CO2 with
the high precision of 1% and resolution of 1 km approximately. In this paper, we aim at quantifying the error associated
with aerosol and albedo over China utilizing the new designed parameters of TanSat. Firstly, the latest specifications of
TSCS are analyzed through the observing simulations as well as the retrieval experiments over some areas in China, where
space-based measurements of CO2 confront the huge challenge induced by atmospheric aerosols which optical depth can
ascend up to more than 1 at wavelengths of 550 nm at certain atmospheric conditions. MODIS aerosol and albedo products
are used in the synthetic measurements. The impacts of both aerosol scattering and surface albedo on CO2 retrieval
accuracy are investigated by applying different retrieval implementation. The errors are estimated for nadir observation
over land with typical solar zenith angle 30° and 60°. Comparisons amongst the three approaches suggest that correctly
treatment of aerosol scattering is necessary to account for the impacts of multiple scattering in order to meet the
requirement of TanSat mission. The development of retrieval algorithm will be continued to the launch of TanSat in late
2015.

An empirical multilinear model was developed for estimating ground-level PM2.5
concentration at city scale (Chengdu, China) using Landsat 8 data. In this model, the improved DDV (dense
dark vegetation) algorithm (V5.2) was used to retrieve aerosol optical thickness (AOT), Image-based Method
(IBM) was used to compute the land surface temperature (LST), and TVDI was calculated to reflect the air
humidity. The three parameters (AOT, LST, TVDI) and in-situ measured PM2.5 (particulate matter) data
were then utilized to establish the empirical model by partial least square (PLS) regression. In the
computation, the band 9, cirrus band, was used to reduce the influence of atmospheric vapor to LST retrieval.
The results show that when considering moisture and temperature, the correlation between AOT and PM2.5
would be efficiently improved; furthermore, moisture shows more impact on the relationship than
temperature. Station record hourly average PM2.5 also shows higher correlation coefficients than 24-hr
average. As a result, PM2.5 concentration distribution across Chengdu was retrieved using this model
developed in this paper. The method could be a beneficial complement to ground-based measurement and
implicate that remote sensing data has enormous potential to monitor air quality at city scale.

Formaldehyde (HCHO) is a significant constituent of the atmospheric chemistry involved in a lot of chemical reactions,
which principal global source is the intermediate oxidation of volatile organic compounds (VOCs). Taking into account
that HCHO basically undergo by photolysis and reaction with hydroxyl radical within a few hours, isoprene together
with other short-lived VOCs and direct HCHO emissions can cause local HCHO enhancement over certain areas, and,
hence, cases with HCHO, that exceed some background level, can be examined as local pollution of the atmosphere by
VOCs.
HCHO has significant specific features in spectral structure of UV absorption cross-section to be measured using the
differential optical absorption spectroscopy (DOAS) technique. Several retrieval algorithms applicable for DOAS
measurements in cloudless were previously developed. A new algorithm applicable for overcast and cloudless sky and its
error analysis is briefly presented in this paper.
In case we know the cloud base height, but don’t know cloud optical depth, the error of HCHO total content retrieval is
about 10-20% for winter season, about 5% for summer season, and about 30-40% for transition season when the ABL is
below the cloud base. In case we know both the cloud base height and cloud optical depth, the error is about 5-10% for
winter season, less than 5% for summer season, and about 25-35% for transition season when the ABL is below the
cloud base. The errors dramatically increase when clouds penetrate into ABL in both cases.

A combination of the invariant imbedding T-matrix (II-TM) method, the improved geometric-optics method (IGOM),
and the pseudo-spectral time domain (PSTD) method provides advanced modeling capabilities to simulate the singlescattering
properties of ice crystals for the entire size parameter range. The downstream applications of the singlescattering
properties simulated from the new modeling capabilities, and, consequently, the bulk radiative properties
render significant improvements, particularly, in remote sensing implementations involving ice clouds. Furthermore, the
single-scattering properties of individual ice crystals are simulated. In addition, a sensitivity study is performed
regarding the application of the single-scattering properties to remote sensing of ice cloud properties based on
spaceborne observations.

The performance of the Community Radiative Transfer Model (CRTM) under ice cloud conditions is evaluated and
improved with the implementation of MODIS collection 6 ice cloud optical property model based on the use of severely
roughened solid column aggregates and a modified Gamma particle size distribution. New ice cloud bulk scattering
properties (namely, the extinction efficiency, single-scattering albedo, asymmetry factor, and scattering phase function)
suitable for application to the CRTM are calculated by using the most up-to-date ice particle optical property library.
CRTM-based simulations illustrate reasonable accuracy in comparison with the counterparts derived from a combination
of the Discrete Ordinate Radiative Transfer (DISORT) model and the Line-by-line Radiative Transfer Model
(LBLRTM). Furthermore, simulations of the top of the atmosphere brightness temperature with CRTM for the Crosstrack
Infrared Sounder (CrIS) are carried out to further evaluate the updated CRTM ice cloud optical property look-up
table.

This paper will focus on the effect of atmospheric conditions on EO sensor performance using
computer models. We have shown the importance of accurately modeling atmospheric effects
for predicting the performance of an EO sensor. A simple example will demonstrated how real
conditions for several sites in China will significantly impact on image correction, hyperspectral
imaging, and remote sensing.
The current state-of-the-art model for computing atmospheric transmission and radiance is,
MODTRAN® 5, developed by the US Air Force Research Laboratory and Spectral Science, Inc.
Research by the US Air Force, Navy and Army resulted in the public release of LOWTRAN 2 in
the early 1970’s. Subsequent releases of LOWTRAN and MODTRAN® have continued until
the present.
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The paper will demonstrate the importance of using validated models and local measured
meteorological, atmospheric and aerosol conditions to accurately simulate the atmospheric
transmission and radiance. Frequently default conditions are used which can produce errors of
as much as 75% in these values. This can have significant impact on remote sensing
applications.

This study introduces the construction of the satellite observation cycling assimilation system in national satellite
meteorological center (NSMC). A typhoon case (1209 Saola ) is chosen to be performed as a testing experiment to check
the operation of the cycling assimilation, with a low resolution . Three experiments are designed: control, ATOVS
microwave observation assimilation and forecasting with cold starting, assimilation and forecasting with warm starting.
Compared with the cold start forecasting, cycling forecasting showed advance in describing more information of the
Tropical Cyclones in detail. As for track and intensity prediction, both the two assimilation experiments were prior to
control experiment. Especially, the cycling experiment is better than cold experiment in the first one day and third day
and the day before landing, but not act well in its peak period, which may suggest that the model couldn’t not match the
description of the typhoon Saola at the full developing period or upgrading develop period, with the low resolution in the
testing experiments, but also can demonstrate well when it develop slowly or in a relatively steady period.

An algorithm for estimating cloud base height (CBH) based on the combination of cloud products from CloudSat
(provides CBH) and Moderate resolution Imaging Spectroradiometer (MODIS) (provides cloud top pressure and cloud
optical thickness) is presented. The relationship among cloud geometric parameters and the feasibility of estimating the
CBH via combining CloudSat and MODIS observations is analyzed. When the cloud top pressure (CTP) and cloud
optical thickness (COT) of a certain point have been obtained by MODIS, its CBH could be estimated by searching the
similar point in CloudSat track, which shares the same CTP, COT and CBH with the estimating point. In the process of
searching the most matching point, an adjusting factor is introduced to uniform the unit of CTP and COT. The retrieval
accuracy of cloud base height is heavily relied on the CBH provided by Cloudsat and the best matching point. Dataexclusion
experiments along the CloudSat track also show a nice performance without the Cloudsat cloud classification
products. And the root mean-square-error is less than 2 km when the exclusion distance is less than 100 km. This method
provides a new approach to render a 3D cloud structure in a wide field.

This paper provides an inter-comparison study of various ground-based cloud retrieval algorithms that have been
developed to obtain cloud water content. The retrieval algorithms are classified into three types, statistical
parameterization algorithm, physical retrieval algorithm, and optimal iteration method. Analyses indicate that physical
retrieval algorithms are theoretically accurate, however, assumptions used in these methods make it challenging for them
to obtain highly reliable results. Empirical parameterization methods are simple and can be easily applied. However,
these methods are generally based on very limited cloud samples for certain types of clouds and locations, they have
much larger uncertainties. In contrast, the optimal iteration method seems to have relatively higher accuracies since the
retrieval results make the forward model simulations match observations. However, the accuracy of optimal iteration
method is highly dependent on the reliability of the forward models and the a priori information.

A recently developed 220GHz incoherent radar has potential for remote sensing of low reflectivity atmosphere targets in
Cloud Chamber. Stepped frequency system is used and bandwidth 10GHz. Preliminary reflectivity measurements of
clouds for ranges between 0.2m~4m in narrow Cloud Chamber. The instrument is briefly described. Highlighting
uncertainties due to highly variable attenuation and signal interference. Then the results of investigations of the
transmitter, receiver, Antennas, as well as the atmospheric Propagation Effects are presented. The results of this effort
demonstrate that the radar is a stable, sensitive, system capable of providing accurate power for clouds.

Extensive studies have illustrated the importance of obtaining exact vertical structures of clouds
and aerosols for satellite and relevant climate simulations. However, challenging exists, for example, in
distinguishing clouds from aerosols at times. Accurate cloud vertical profiles are mainly determined by cloud
bases and heights. Based on the ground-based lidar observations in Hefei Radiation Observatory (HeRO), the
vertical structures of clouds and aerosols in Hefei area(31.89°N，117.17°E) during May 2012-May 2014
have been investigated. The results show that the cloud fraction in the autumn and winter is less than that in
the summer and spring, and is largest in the spring followed by the summer. The cloud fractions in the autumn
and winter are comparable. The low cloud accounts for the most portion of the total. Compared with the cloud
of the other heights, the high cloud is the least in the winter. Nearly 50% of the total vertical profiles can be
detected by lidar as clouds and the proportion of the cloud of different heights seems to be stable annually.
The fraction of low cloud is nearly 45%, medium cloud is nearly 35%, and high cloud is nearly 20%. In
comparison with the results derived from CALIPSO, it is found that high cloud is usually missed for the
ground-based lidar, while low cloud is usually omitted for the satellite observations. A combination of
ground-based and space-borne lidar could lead to more reliable results. Further analysis will be performed in
future studies.

As the similar cross track scanning mode of the measurements of Tropical Rainfall Measuring Mission’s (TRMM)
Precipitation Radar (PR) and visible and infrared scanner (VIRS) results in near instantaneous synchronization in
observing the same target, it is feasible to jointly use these two measurements to explore exactly the spectral
characteristics of precipitating clouds (PCs). Meanwhile, it will effectively improve and extend the abilities to identify
PCs using visible/infrared (VIR) measurements, because VIR sensors can be loaded aboard geostationary satellites with
the opportunity of high sampling frequency. In this paper, PR and VIRS onboard TRMM are respectively used to capture
PCs and identify their spectral signals during the Meiyu over the Yangtze-Huaihe River Valley from 1998 to 2007.
Visible/infrared signals for PCs, which are the reflectance at 0.63μm and 1.6μm (referred to as RF1 and RF2,
respectively), and the equivalent brightness temperature of a blackbody at 3.7μm, 10.8 μm and 12.0μm (referred to as
TB3, TB4 and TB5, respectively), were derived by TRMM VIRS. Firstly, characteristics of rain rate, rain top and
vertical profiles for stratiform and convective precipitations are investigated during the Meiyu periods. Moreover, VIRS
individual-channel signals, and multi-channel indices for PCs of the Meiyu are evaluated quantitatively. Finally, revealed
by long-term mean values, the differences of spectral signals between stratiform and convective PCs and their
relationships with surface rain rates are explored.

Cloud is one of the most common influences in remote sensing imagery. Because of cloud interference, much
important and useful information covered by cloud cannot be recovered well. How to detect and remove cloud
is an important issue for wide application of remote sensing data. A novel and effective method is proposed in
this paper to detect cloud in remote sensing image using light transmittance. Light transmittance is employed
to detect the cloud and also determine its corresponding thickness distribution. First, a cloud optical model is
defined based on the airlight-albedo model. Second, preliminary cloud light transmittance is estimated using dark
channel prior and then refine the result through guided filtering algorithm. Finally, we use light transmittance
to detect cloud region by thresholding and obtain detailed information about the distribution of cloud thickness
through mapping light transmittance of the cloud region into a gray image. Our method has been tested on real
remote sensing images with clouds. Compared with the existing methods, experimental results have proved the
better efficiency of our method in cloud detection.

The interannual variations of the Arctic total column ozone in spring from 1979 to 2011 are analyzed using measurements
of the Total Ozone Unit (TOU) onboard the second generation polar orbiting meteorological satellite of China, Fengyun-3
(FY3/TOU) and other satellites. It is found that the interannual variations are very distinct and are connected with the
stratospheric temperatures (with a correlation coefficient of 0.75). The daily and monthly variability of Arctic ozone are
extremely different in the anomalous year. The chemical impact is pronounced in the strongest ozone loss years (1997 and
2011), but not obvious in the weakest ozone loss years (1999 and 2010). The daily variations in the weak ozone loss years
could be regulated by the weather system process. The Arctic ozone variations are modulated by the atmospheric
circulation, accounting for change of AO, polar vortex and stratospheric temperature. When AO index is positive and the
polar vortex is stronger with colder stratosphere, the Arctic ozone loss is larger. When AO index is negative and polar
vortex is weaker with warmer stratosphere, the Arctic ozone loss is smaller.

As we know, China is the largest developing country and the United State (US) is one of the most developed countries of
the world. Due to significant differences of the developmental levels between China and the US, different pollutants
emissions may be performed. It is found that aerosol optical depth (AOD) over China is much higher than that over
America. Since China and the US locate in westerly wind belts, it is feasible to examine the relationship between
different AOD and cloud parameters over land and offshore area of the two countries. In this paper, cloud effective
radius (CER), liquid water path (LWP) and AOD derived from the Moderate Resolution Imaging Spectroradiometer
(MODIS) and circulations supplied by NCEP/NCAR reanalysis data from 2000 to 2013 are employed to explore the
relationships between AOD and CER under different LWP levels. Results indicate that there is a clear negative
relationship between AOD and CER in different LWP levels over the offshore area contrary to the insignificant
relationship over land or the open sea. It suggests that aerosol indirect effects are more obvious over the offshore area.

The air quality indicator approximated by satellite measurements is known as an atmospheric particulate loading, which
is evaluated in terms of the columnar optical thickness of aerosol scattering. This paper is attempting and estimating
PM10 concentration by using Landsat 5 satellite data and validating these with air pollution measurements in
Ulaanbaatar, Mongolia. We have been used the empirical method which based on multispectral algorithm PM10 model.
Results from this research on concentration of PM10 in Ulaanbaatar city have been included.

Cloud detection is a key work for the estimation of solar radiation from remote sensing. Particularly, the
detection of thin cirrus cloud and the edges of thicker cloud is critical and difficult. To obtain accurate
estimates of cloud cover of MTSAT-1R image, we propose an effective cloud detection algorithm for
improving the detection of thin cirrus cloud and the edges of thicker cloud. Using the brightness
temperature difference (BTD) and lookup table to identify cloud-free and cloud-filled pixels is not
sufficient for MTSAT-1R data on the region of China. Therefore, a new lookup table (LUT) is made by
extending the original one. On the basis of the exiting method, in order to apply to the MTSAT-1R
satellite data in China region, we expand the scope of the latitude and extend the applicable scope of
satellite zenith angle. We change the interpolation method from linear mode to nonlinear mode. The
evaluation results indicate that our proposed method is effective for the cirrus and the edges of thicker
cloud detection of MTSAT-1R in China region.

Aerosol Optical Depth (AOD) is one of the key parameters which can not only reflect the characterization of atmospheric
turbidity, but also identify the climate effects of aerosol. The current MODIS aerosol estimation algorithm over land is
based on the “dark-target” approach which works only over densely vegetated surfaces. For non-densely vegetated
surfaces (such as snow/ice, desert, and bare soil surfaces), this method will be failed. In this study, we develop an algorithm
to derive AOD over the bare soil surfaces. Firstly, this method uses the time series of MODIS imagery to detect the “
clearest” observations during the non-growing season in multiple years for each pixel. Secondly, the “clearest”
observations after suitable atmospheric correction are used to fit the bare soil’s bidirectional reflectance distribution
function (BRDF) using Kernel model. As long as the bare soil’s BRDF is established, the surface reflectance of “hazy”
observations can be simulated. Eventually, the AOD over the bare soil surfaces are derived. Preliminary validation results
by comparing with the ground measurements from AERONET at Xianghe sites show a good agreement.

In order to obtain the DEM terrain elevation is usually calculated by photogrammetry method, which is based on
establishing the correspondence between stereo images. However it is difficult to find the salient correspondence features
in textureless or difficult texture regions such us mountain, snow or desert area. To solve the problem we use the shape
from shading (SFS) to extract the surface elevation of the textureless area from one image. SFS is a very ill-posed and
underconstrainted problem. The primary overwhelming assumption SFS is he Lambertian-based image irradiance
equation with constant albedo for each ground point. Due to the physics of imaging process, satellite images do not
exhibit constant albedo properties and the distribution of the surface albedo is not uniform. To address the problems
above, we develop a shape recovery algorithm for the areas of weak texture or difficult texture. Given coarse DEM in
weak texture area and optical image of higher spatial resolution we estimated the albedo for each ground points using
relief image calculated from coarse DEM. With the known sun zenith and azimuth angles the image irradiance map is
formulated as a general static Hamilton-Jacobi equation and is solved using a fast sweeping numerical method with
several ground truth elevation points as boundary conditions. Landsat 7 ETM+ band 4 image and SRTM 90m global
DEM data as reference surface elevation map are used to evaluate the algorithm proposed. Experiments indicate the
effectiveness of the proposed method for surface elevation estimation for textureless area.

With the stable increase of carbon dioxide (CO2) concentrations, space based measurements of CO2
concentration in lower atmosphere by reflected sunlight in near infrared band has become a hot
research topic at present. Recently, the instruments sensitive to total CO2 column data in near-surface
have become available through the SCIAMACHY instrument on ENVISAT and TANSO-FTS on
GOSAT. The developing hyper spectral CO2 detector in China carried by TANSAT will be launched
in late 2015. Hyper spectral CO2 detector is designed to provide global measurements of CO2 in
lower troposphere. It employs high resolution spectra of reflected sunlight taken simultaneously in
near-infrared CO2 (1.61μm and 2.06μm) and O2 (0.76μm) bands.
Associating climate change with the observation requirements of carbon sources and sinks, the
feasibility of making CO2 column concentration measurements with high-resolution and
high-precision is studied by high resolution atmosphere radiation transfer model. The effects of key
specifications of the hyper spectral CO2 detector such as spectral resolution, sampling ratio and
sign-to-noise ratio (SNR) on CO2 detection are analyzed combining the scientific requirements of
CO2 measurements of China.
The typical characteristics of hyper spectral CO2 detector on TANSAT are grating spectrometer
and array-based detector. To achieve the column averaged atmospheric CO2 dry air mole fraction
(XCO2) precision requirements of 1×10-6-4×10-6, hyper spectral CO2 detector should provide high
resolution at first to resolve CO2 absorption lines from continuous spectra of reflected sunlight.
Compared to a variety of simulated spectral resolutions, the spectral resolution of hyper spectral CO2
detector on TANSAT can resolve CO2 spectral features and maintain the moderate radiance
sensitivity. Since small size array detector-based instruments may suffer from undersampling of the
spectra, the influences of spectral undersampling to CO2 absorption spectra are studied, the results
indicate that sampling ratio should exceed 2 pixels/FWHM to ensure the accuracy of CO2 spectrum.
Signal-to-noise ratio is one of the most important parameters of hyper spectral CO2 detectors to
ensure the reliability of CO2 signal. SNR requirements of CO2 detector to different detection
precisions are explored based on the radiance sensitivity factors. The results show that it is difficult
to achieve the SNR to detect 1×10-6-4×10-6 CO2 concentration change in the boundary layer by solar
shortwave infrared passive remote sensing, limited by the instrument development at present.
However, the instrument SNR to detect 1% change in the CO2 column concentration is attainable.
The results of this study are not only conductive to universal applications and guides on developing
grating spectrometer, but also helpful to have a better understanding of the complexity of CO2
retrieval.

Proc. SPIE 9259, Possibility of relationship between the yellow sand and the foot-and-mouth disease in Miyazaki Prefecture, Japan in March 2010 by using MODIS images, 92591V (8 November 2014); doi: 10.1117/12.2069166

In Miyazaki Prefecture, Japan, the O-type foot-and-mouth disease (FMD) appeared and spread from March to July, 2010.
The first infected livestock by FMD virus was detected on March 26, 2010 at Tsuno Town in Miyazaki Prefecture. The
O-type FMD was found on March 14 at the suburb of Lanzhou City in Gansu, and on March 25, 2010 in Shanxi, China.
The duration of FMD virus incubation is 2 to 8 days. Maki et al. (2011, 2012) presumed the cause of the first FMD in
Miyazaki as follows: The yellow sand adhered with FMD virus was transported from Gansu to Miyazaki by global
westerly winds. In this paper, we investigate whether the yellow sand generated in Gansu flew to Miyazaki in March,
2010 by using MODIS data of Terra and Aqua satellites. True-color mosaic images, AVI mosaic images and T11 mosaic
images from China to Japan are made and examined. The aerosol vapor index (AVI) is defined as AVI=T12-T11, where
T12 and T11 are the brightness temperatures at 12μm and 11μm wavelength, respectively. The AVI can detect the dust
and sandstorms (DSS, i.e., yellow sands) in satellite images both at daytime and night. AVI values are classified into six
levels from 0 to 5. From AVI images, DSS existed in the vicinity of Lanzhou on March 19, and in the south area of
Shanxi on March 20, and in the vicinity of Tsuno Town on March 21. If Maki et al. are right, the cause of the first FMD
in Miyazaki in March 2010 is that DSS generated in Gansu on March 19 flew to Miyazaki on March 21.

Sulfur dioxide (SO2) has a significant impact on the urban environment pollution and global climate.
Compared with regional ground monitoring networks, satellite remote sensing technology provides
an unprecedented advantage for continuous, large spatial and short-revisit monitoring for
atmospheric SO2. Approaches for retrieval of SO2 from ultraviolet satellite observations have been
developed and applied to detection of volcanic SO2 and regional emissions. However, these retrieval
algorithms do not consider the temperature variation effect on SO2 retrievals, and simply use the
absorption coefficient at a constant temperature as inputs for SO2 retrievals. In this study, hyperspectral
OMI measurements were used to analyze the temperature effects on the retrieval of SO2
columns. Results of DOAS algorithm showed that with increasing SO2 concentration, the retrieval
errors caused by temperature effects accumulated, and the differences in SO2 columns increased to a
maximum of ~25 DU (SO2 column of 293K: ~65 DU). Therefore, atmospheric temperature is an
important factor which has significant influence to high precise atmospheric SO2 retrievals.

Using the Constellation Observation System of Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO)
data from December 2008 to November 2009, and the contemporaneous high vertical resolution radiosonde data, the
accuracy of COSMIC RO data in the Qinghai-Tibet Plateau is evaluated. Results show that there is a good consistency
between COSMIC refractivity and those calculated from the radiosondes between 5 and 30km altitude both in day and at
night. Their annual mean fractional refractivity difference is -0.474%, and standard deviation is 1.056%. It illustrates that
the RO data is reliable and may be effectively applied in the Qinghai-Tibet Plateau for the weather and climate research,
or as a reference data to evaluate other observations and the model’ simulations.

In this paper, we demonstrated the best fitting window and spectral resolution to retrieve NO2 Vertical Column Density
(VCD) from space borne spectrometer in ultra-violet. The reflectance at TOA was simulated with atmospheric radiation
transfer model SCIATRAN, which takes both molecules absorption and aerosol multiple scattering into consideration.
The NO2 VCD was retrieved using the Differential Optical Absorption Spectroscopy (DOAS) method. There are five
kinds of factors has been taken into the NO2 VCD retrieval sensitivity analysis: fitting window and spectral resolution,
aerosol optical thickness, surface albedo, NO2 concentration in the lower troposphere and sun-satellite geometry. The
results showed that DOAS method cannot well filter the aerosol scattering. High surface reflectivity can strengthen the
signal at TOA and thus enhance the retrieval accuracy. The AMFs become larger dramatically when the sun or satellite
zenith angels are above 70 degree, while the relative azimuth angel affects little in the AMF.

Spatial heterogeneity of the animal-landscape system has three major components: heterogeneity of
resource distributions in the physical environment, heterogeneity of plant tissue chemistry,
heterogeneity of movement modes by the animal. Furthermore, all three different types of
heterogeneity interact each other and can either reinforce or offset one another, thereby affecting
system stability and dynamics. In previous studies, the study areas are investigated by field sampling,
which costs a large amount of manpower. In addition, uncertain in sampling affects the quality of field
data, which leads to unsatisfactory results during the entire study. In this study, remote sensing data is
used to guide the sampling for research on heterogeneity of vegetation coverage to avoid errors caused
by randomness of field sampling. Semi-variance and fractal dimension analysis are used to analyze the
spatial heterogeneity of vegetation coverage at Heihe River Basin. The spherical model with nugget is
used to fit the semivariogram of vegetation coverage. Based on the experiment above, it is found,
(1)there is a strong correlation between vegetation coverage and distance of vegetation populations
within the range of 0～28051.3188m at Heihe River Basin, but the correlation loses suddenly when the
distance greater than 28051.3188m. (2)The degree of spatial heterogeneity of vegetation coverage at
Heihe River Basin is medium. (3)Spatial distribution variability of vegetation occurs mainly on small
scales. (4)The degree of spatial autocorrelation is 72.29% between 25% and 75%, which means that
spatial correlation of vegetation coverage at Heihe River Basin is medium high.

With the development of GNSS systems, it has become a tendency that radio occultation is used to sense the Earth’s
atmosphere. By this means, the moisture, temperature, pressure, and total electron content can be derived. Based on the
sensing results, more complicated models for atmosphere might come into being. Meteorology well benefits from this
technology. As scheduled, the BD satellite navigation system will have a worldwide coverage by the end of 2020. Radio
occultation studies in China have been highlighted in the recent decade. More and more feasibilities reports have been
published in either domestic or international journals. Herein, some scenarios are proposed to assess the coverage of
radio occultation based on two different phases of BD satellite navigation system. Phase one for BD is composed of
GEO,IGSO and several MEO satellites. Phase two for BD consists mostly of 24 MEO satellites, some GEO and IGSO
satellites. The characteristics of radio occultation based on these two phases are presented respectively.

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Journal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews